158 citations · 385 across the 5 of their papers we have counts for
5 papers
Measuring the Reliability of Reinforcement Learning Algorithms
Stephanie C. Y. Chan, Samuel Fishman, John Canny +2
Lack of reliability is a well-known issue for reinforcement learning (RL) algorithms. This problem has gained increasing attention in recent years, and efforts to improve it have g…
From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following
Justin Fu, Anoop Korattikara, Sergey Levine +1
Reinforcement learning is a promising framework for solving control problems, but its use in practical situations is hampered by the fact that reward functions are often difficult…
Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC
Sungjin Ahn, Anoop Korattikara, Nathan Liu +2
Despite having various attractive qualities such as high prediction accuracy and the ability to quantify uncertainty and avoid over-fitting, Bayesian Matrix Factorization has not b…
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
Anoop Korattikara, Yutian Chen, Max Welling
Can we make Bayesian posterior MCMC sampling more efficient when faced with very large datasets? We argue that computing the likelihood for N datapoints in the Metropolis-Hastings…
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
Sungjin Ahn, Anoop Korattikara, Max Welling
In this paper we address the following question: Can we approximately sample from a Bayesian posterior distribution if we are only allowed to touch a small mini-batch of data-items…